From the course: AI Trust and Safety: Navigating the New Frontier
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Developing feedback loops for continuous improvement
From the course: AI Trust and Safety: Navigating the New Frontier
Developing feedback loops for continuous improvement
- Your AI system needs to evolve as users interact with it. Here are the methods to make feedback a cornerstone of improvement. First, collect feedback. You need to make it easy for users to tell you what's working and what's not. Add a feedback button or monitor social media for organic comments. Then you can analyze feedback. Group similar concerns to find patterns. If multiple users flag the same issue, that's a priority. Based on insights from the feedback, you can adjust your system. For example, tweak prompts or update training data to fix recurring issues. Last, test changes. AB testing here is your friend. Roll out updates to a small group first and see if the improved performance without causing new problems. So what's the main insight here? A transparent feedback process not only improves the model, but also builds user trust by showing their concerns matter.
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